Risk Warning Model of Postoperative Delirium and Long-term Cognitive Dysfunction in Elderly Patients

NCT06423547 | Recruiting

• 1 other identifier: 62376168
• Study Type: observational
• Target: 10,000
• Locations: 1 country
• Sites: 1

Brief Summary

The incidence of postoperative delirium in elderly patients is high, which can lead to long-term postoperative neurocognitive disorders. Its high risk factors are not yet clear. At present, there is a lack of early diagnosis and alarm technology for perioperative neurocognitive disorders, which can not achieve early intervention and effective treatment. By artificial intelligence and autonomously evolutionary neural network algorithm, relying on multi-source clinical big data, we explored the use of Bayesian network to optimize the anesthesia decision-making system in enhanced recovery after surgery, and established risk prediction model for perioperative critical events. It is expected that this method will also help to establish a risk prediction model for postoperative delirium and long-term postoperative neurocognitive disorders. This project plans to collect the perioperative sensitive parameters of anesthesia machine, multi-parameter monitor, EEG monitor,fMRI and HIS system, to explore the evolution process of data characteristics by feature fusion.We also plan to quickly screen key perioperative risk characteristics of postoperative delirium from massive clinical data through feature selection, to explore the high risk factors of long-term postoperative neurocognitive disorders developing from postoperative delirium. Finally, with multi-center intelligent analysis，the risk prediction model of postoperative delirium and long-term postoperative neurocognitive disorders will be constructed.

Conditions

Postoperative Delirium; Postoperative Neurocognitive Disorder; Surgery

Keywords

postoperative delirium;; postoperative neurocognitive disorder;; risk prediction model;; artificial intelligence;; evolutionary neural network

Outcome Measures

Primary Outcomes (2)

• Screening for risk factors of perioperative cognitive dysfunction

  The feature selection technique in artificial intelligence was used to screen and analyze data from a large dataset of clinical care after fusion The risk factors with the highest probability of PND occurrence can be screened from a large number of characteristics,By screening the risk factors that have the highest correlation with the probability of POD occurrence, combined with the comparison of fMRI imaging data of different groups of large sample size POD patients with long-term conversion to pNCD group and non-PNCD group, the brain network mechanism and perioperative high risk factors of POD conversion to long-term cognitive dysfunction were further explored.

  2024.4.1-2027.12.31

• Establish a prediction system for adverse brain function events

  The monitoring data of surgical patients contains a large amount of medical information, and the analysis and modeling of the data can provide effective early warning and intervention. The project intends to adopt EEG time-frequency feature extraction and analysis, EEG micro-state analysis, and brain network analysis, and adopt feature fusion technology to fuse various features into unified features of patients. On this basis, a prediction model of adverse brain function events based on domain adaptation algorithm was constructed to realize real-time tracking, early diagnosis and early warning of postoperative delirium and long-term cognitive dysfunction in elderly patients

  2025.1.1-2027.12.31

Study Arms (1)

postoperative delirium(POD) and postoperative neurocognitive disorder(pNCD)

Delirium (CAM scale ) was assessed 7 days after surgery and divided into POD and non-POD groups; one of the above scenarios indicated postoperative delirium;The patients in the POD group were evaluated for cognitive function at 1 month and 12 months after surgery to determine whether pNCD occurred. The patients in the POD group were further divided into pNCD subgroup and non-PNCD subgroup, and EEG data collection and fMRI scanning were performed

Other: no intervention

Interventions

no intervention OTHER

this is an observation study,no intervention

postoperative delirium(POD) and postoperative neurocognitive disorder(pNCD)

Eligibility Criteria

• Age: 65 Years - 100 Years
• Sex: all
• Healthy Volunteers: No
• Age Groups: Older Adult (65+)
• Sampling Method: Probability Sample

Study Population

Patients 65\~100 years of age who have undergone surgical anesthesia

You may qualify if:

• Patients ≥65 years of age who have undergone surgical anesthesia; Sign informed consent

You may not qualify if:

• Inability to complete cognitive function assessment; Illiteracy, hearing impairment or visual impairment; He has a history of epilepsy, depression, schizophrenia, Alzheimer's disease and other psychiatric and neurological diseases

Sponsors & Collaborators

• Xuanwu Hospital, Beijing: lead
• Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences: collaborator

Study Sites (1)

Xuanwu Hospital, Capital Medical University

Beijing, 100053, China

RECRUITING

Related Publications (2)

• Patel A, Zhang M, Liao G, Karkache W, Montroy J, Fergusson DA, Khadaroo RG, Tran DTT, McIsaac DI, Lalu MM. A Systematic Review and Meta-analysis Examining the Impact of Age on Perioperative Inflammatory Biomarkers. Anesth Analg. 2022 Apr 1;134(4):751-764. doi: 10.1213/ANE.0000000000005832.

  PMID: 34962902 BACKGROUND

• An Y, Zhao L, Wang T, Huang J, Xiao W, Wang P, Li L, Li Z, Chen X. Preemptive oxycodone is superior to equal dose of sufentanil to reduce visceral pain and inflammatory markers after surgery: a randomized controlled trail. BMC Anesthesiol. 2019 Jun 11;19(1):96. doi: 10.1186/s12871-019-0775-x.

  PMID: 31185942 BACKGROUND

MeSH Terms

Conditions

Emergence Delirium

Condition Hierarchy (Ancestors)

Delirium; Confusion; Neurobehavioral Manifestations; Neurologic Manifestations; Nervous System Diseases; Postoperative Complications; Pathologic Processes; Pathological Conditions, Signs and Symptoms; Signs and Symptoms; Neurocognitive Disorders; Mental Disorders

Study Officials

• lei zhao

  xuanwu hospital of capital medical university,Beijing

  STUDY CHAIR

• yong yang

  Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences

  PRINCIPAL INVESTIGATOR

• yi an

  xuanwu hospital of capital medical university,Beijing

  PRINCIPAL INVESTIGATOR

• xia li li

  xuanwu hospital of capital medical university,Beijing

  PRINCIPAL INVESTIGATOR

• yang liu

  xuanwu hospital of capital medical university,Beijing

  PRINCIPAL INVESTIGATOR

• yi shu yang

  xuanwu hospital of capital medical university,Beijing

  PRINCIPAL INVESTIGATOR

Central Study Contacts

lei zhao

CONTACT

+8613811035886; zhaoalei@sina.com

xia li li

CONTACT

+86818810616341; 935496838@qq.com

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: PROSPECTIVE
• Target Duration: 1 Year
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: May 15, 2024
• First Posted: May 21, 2024
• Study Start: July 30, 2024
• Primary Completion (Estimated): December 31, 2027
• Study Completion (Estimated): December 31, 2027
• Last Updated: April 3, 2025

Record last verified: 2025-03

Data Sharing

• IPD Sharing: Will not share

Locations

CN (1)